{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://csdms.colorado.edu/wiki/ESPIn2020\"><img style=\"float: center; width: 75%\" src=\"../../media/ESPIn.png\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Writing stand-alone Python scripts\n",
    "\n",
    "We start out using the Jupyter (iPython) notebooks in these lessons because they are a friendly environment to try out individual commands and write short scripts. The Jupyter notebooks, however, can only run inside their specific graphical environment. Stand-alone Python programs are text files with the extension `.py` that are run from the command line or within an IDE (Integrated Development Environment) such as Spyder. Your Python code has to be in `.py` files to run on an HPCC.\n",
    "\n",
    "The Jupyter notebooks are valuable in the early stages of your code development workflow, where you gradually build your program by tinkering with the code and exploring the data. However, you should quickly move your prototype code into stand-alone scripts that are platform independent and better suited for version control.\n",
    "\n",
    "Before we dive further into Python, we are going to convert the scripts we wrote in the first lesson into stand-alone Python scripts that can run from the command line. We will first run this script locally (in your own computer).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look back at the script we wrote earlier that plots the West-to-East topographic profiles across our data. You should copy the code from the Jupyter notebook you were working in before to a new notebook to make sure nothing sneaks into the script:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mSY0mRRq3a1BXGlRtkKf9842f8/a/b9Pos0Z8u+XbPOeTM5KZv29+keYy2OCjj/R3ZfJkqF69ZObs2VMHkfbvDw8/rGM0DGUKY7MoKgEBjh+zTRu9Utm/32FD/rb7Nx6c/yDpWelsOr2Jv+//O9f5O1vfSYBfAAObDsRL2f/McCbxzAXPqUxLZh47yLLDyxi5cCQnzp9g7Yi1XN3garvnLJOEhcHrr8Ptt8PQoSU7d8WKMH++zrT83Xdw551u5cVncC5mZVEUnnxSJ2lzNH5+sGuX9kJxEN2CupFp0bmnlh1ZxpbwLbnO+5bz5cZmNzpEUQAEVgxk5u0z6dmwJ31D+tKuTrtc54/GHeX4+eMIwsiFI0nPSnfIvGWKlBSd56lKFZg0yTUyVKgAf/wBf/5pFEUZw6wsCsvZs3rZX7t2kbvuO7uPpjWa4u2Vj52jZUs7hMtLcLVgbmlxC+EJ4YzrNo72dex09S2Acl7luLXlrdza8labgXxX1roSgAC/AJ7t/iw+Xj5OladU4uenDcyZmcX6HjqM7GJfMTHw/vvw5pvaEG4o1ZiVRWFZsUJ7K11/fZG6pWWm0embTgR+GMh9c+8jOukytcGTkrTR8vffizR+amYqT/z5BDvO5E1KOH3odNY/vJ5hrYflr6gcTKXylfK0tanVhkkDJ3H86eOM6DACZX0qzbJk5Vn1GGyQnbOpSRNo0cK1smSzZo1ODTJ2rKslMZQARllcQlxqHJ+u/5TNpzfnPvH337rIUT4us7bybK09sZbkjGTiUuNYfmQ5VStUtd25YkUdpLdoUaFlPXH+BL1+7MVXoV9x66xbiU2JzT2kj42soy6ism9lRncZfUGRWMTCt1u+pcWXLej1Y688shsu4ZVXdBoOd0prP3SozkIwZYpOgWMo1RhlcQmJ6Yk88/czdPmuC5+s/+TiifXroWdPUiTDZr/Rf46m/dftWXZ4Wa72mJQY6lWuB8DIjiMp732Z5bpSOhvt0qUXC9UUQHJGMvvPaqP4kdgjTN3hOdG2CsXk0MkcijlESmYKkza5aA/eEzh/Xud9qlDBeTEVxeWtt3Q2gieeKBk3XoPLMMriEir5XNxCeW7ZcxyOOQxZWfzaRuh21Q7qfVKPuNS8Nben7ZzGzsid3Pzbzaw8uvJC+x2t7+DUuFOEjgxldJfRefotOrCI8avGYxGLLlQTEQGrVxdK1haBLZh6y1R8vHyYNHASY7t6znaAUooxXcYA2o5hyxXXYGXKFB2H88ILrpYkL76+Wr4jR7T9wlBqcbPHFNfjX96fmhVr4lvOl9ta3qb3+r292TRiABs3fgap8MuuX3jiqidy9UvJ0E9VWZYsDsccpm9I3wvnlFJ0qtcpz1y7Indx9+93k5ieyLYz25g9eBoVAgN1jYt+/Qol79CWQzk85rBH3mzvb3c/EYkRPN75car7lVC8gKeRmqqzE/fvDx07uloa2/TpozPfDhniakkMTsQoi0vw8fZh/5P7qVqhai630gMxBy4cbw7Pbc8QEb67+Tsq+lSkZ8OeF7adCmLixokkpuuSlquOriI0NoyeL72kVxcWC3gVbuHniYoCtAfVS71esnnux20/0v+K/tSvUr+EpXIzpk6FyEj3XFXkZORI/TPbEF/I767BczDFjwrD6NEcOr2Lwx+9zBU1rihyxPPlyLRk8sLyF/jr0F/MGTaHFoH5e7msOLKC33b/xmcDPrPpcVRaWHRgETf9ehNeyotRHUfx1eAyXEkwJkbnIxs1yv3jGs6d00WTHn5YvwweyeWKHxllURg6dNB+7X/95ZjxLiE9Kz234VtEG7obNboQfxGdFE3bKW05k3iGFoEtmHfnvAKViyeSlplG68mtORx7GIDXr32d8b3Hu1gqQ6EQ0U4aYWG6znfNmq6WyFAMTKW84pKWBrt3O3W/OI+HVHw8cucwePXVC03Td07nTOIZQHtYVatQzWnyuJJyXuV4u+/b9Anug7fyZkSHEXmuefffd3n7n7c5Fnes5AUsSd5/H+bMcbUUhUcpbexOSIBx41wtjcHRiEipfHXq1EkcQmioCIjMnu2Y8QrB+pPrpc9rwXKoOiLbtomIiMVikR+3/Sj+7/jLXwf/KjFZXEl4fHietvjUeKnybhVhAsIE5OT5ky6QrASIiBDx9RV5/HFXS1J0XntN/89MmeJqSQzFAAgVG/dUs7IoiK1b9c8S8kQ5k3iG22bdxip1jKZj4L9nh0FaGkophrcfzrGxx7ihyQ0lIourqVu5bp62X3b9QnxaPACtarYiqEpQSYtVMnz4oU5F/swzrpak6Lz6qi7k9e23hY4ZMrg/TlMWSqkKSqlNSqkdSqkwpdTrl5z/n1JKlFKBOdpeVEodUkrtV0rdkKO9k1Jql/XcRKVK0NLXsCHccw+EhJTIdGuOrbmw3VSnfA26rD6Ya0kfUNEJWW89iIc6PMT0odMZ2GQgt7W8Lc/5yMRIxq8aT1pmmgukcxBRUfDVV3DvvTq9h6fh7Q2//grLljm27ovBtdhabjjiBSjA33rsA2wEulnfNwCWAseBQGtbK2AH4AuEAIcBb+u5TUB365hLgIEFze+wbSgX8M+xf6T/9P7yyopXRF55RWTWLFeL5BFYLBa5deatwgSkx/c9JDIx0uFznDx/Ut5Y/YbM3D1TwqLC8pyPT42X+NR4+yZ57jkRLy+RffvsG8cdSEgQefJJkeXLRbKyXC2NoRBQ0ttQ1nkTrW99rK9s16tPgedyvAcYAvwmImkichQ4BHRRStUFqojIeusHmQbc4iy5c5GRAdGXSfznRHo16sXS+5byRp83dEbPO+7QJxxY76I0suLoCubunQvAupPr+Gn7Tw6fY0v4FsavHs+dc+7k1VWv5jm/YP8C6nxch9dWvVb8NOzt28Nzz0Hz5nZK6wYcPw5z58J11+m0IH//XXAfg1viVJuFUspbKbUdiAKWichGpdTNwGkRuTRNan3gZI73p6xt9a3Hl7bbmm+UUipUKRUa7Yib/PbtUKuWTvDnAnLttm3apIskjRsHyckukcfd6RfSj4+u/wiF4oUeL/B/V/+fXePZutnvO7vvwnHtSnnThO8/t5/kjGTe+OcNhs4sZnGiu++Gd98tXl93o3VrOHQIpk/XqUEGDoTPPiuxevMGx+FUZSEiWSLSHghCrxLaAi8DthznbdkhJJ92W/N9IyKdRaRzTUf4eGfHabRta/9Y9tKpEzz2mP5Ha9sWNm50tURuh1KKZ69+lp2P7+Sdfu9gj2nrWNwxgj8LZtKmSWRZLhppuwV149nuzzK42WA61OmQp19MSsyF4+zcV4UmKUknDExMLPhaT8LPD+67T39nhwyBGTMg3RS/8jRKJN2HiMQppVajt5pCgB3Wf+QgYKtSqgt6xZAzb0UQEG5tD7LR7nw2b9aBRQ0blsh0+eLtDV98AbfeqqNjr7tOpzPv3TvvtRkZsGSJTmkNugznnj16W6NHD+jbV9cTL6W0qdXG7jHGLBlDRGIETy15is3hm5l6i87o2zu4N72DbfzOrUwcOJGmNZpyOPZwHq81i1iIS42jhl8N251/+klXY2zXTte8Lm34++u4kfPn9Srj/HmtIOsVLj2OwbU40xuqplKqmvXYD7gO2CYitUQkWESC0Yqgo4icARYAdymlfJVSIUBTYJOIRAAJSqluVi+oB4A/nCX3BUTg33+hSxf3SrPQpw+sWwdBQbBtm25bsULXZL7tNm3fCAmBW27RwVGgU1tv3Qpvv63LcgYFaQVShpixc8YFe0ZBxKTEsP/cRfvQo50eLfQ8XsqLsd3GMnHgxDznlhxcQtAnQYxZMsZm5mK++06vIHv0KPR8HoeXF1S3Jo0cNUp7ew0apF2Fjx1zqWiGArBl9XbEC2gLbAN2AruB8TauOYbVG8r6/mW0F9R+cng8AZ2tYxwGJmFNU5Lfy25vqLAwHVj01Vf2jeMs4uNFLBZ9PHeuSMuWIq1bizRvLnLzzSKLFl08n01iosiCBSJDhog8+GDusUopyenJ8sgfjwgTkJaTWkqWpXAeOakZqfLmmjflqcVPOUyW66ddfyGY8K45d+U+mf19++wzh83n9hw4IDJ6tEiLFvqzV6okMmOGq6Uq83AZbyiTG+pyxMVpL45Bg6BOHccJ5i5kZ7XdskVvS/3vfzoArFLpSlAYmxJLo88akZCuV1lz7pjDba3yxmc4m+SMZHr80IPtZ7bj6+3L7id2505I+fLL8N57cPp06fy+FcTRozB8OFxzjfYANLiMy+WGMinKL0e1aromdmklO4V0tWra/jF+PPz4I/z554XkhaWB6n7VebLLk7y/7n3uanMXrWu1dokcFX0qsnXUVubunUtkUmTezMVHjuj67mVRUYDeOl250gTxuTFmZWGL8HCYN0+7MNa4jDGytPHPP7pSX1oazJ9v23DuoZxLPkdsamy+qeVFhAPnDtA80DWxDetPrmfu7tm80Pvlsh2lL6K/fxaLtsEZShyTdbYozJunvVKiolwtSclxzTWwYQPUratjOkoRARUDCqxB8vOun2k9uTXPLn2WhLSEEpJME318D8PmDOOjTZ/SeGJjNpzaUKLzuxVKaWP322+7WhLDJRhlYYu5c7WbaYvSVy8iX4KDtbvw//6n3x8+XCaCp2JTYvm/Zf9HlmTxyYZP+HTDpyU3+bx5fDzqSk7F67hT//L+tKvdrkhDHIk9wt+H/2b1sdV5zu2K3MWYJWOYsHqC56R0HzhQB8TGxBR8raHEMMriUqKiYPXqiyk2yhqVKumnu1OndNGnoUPhzBlXS+VQLGJh46mLQY0VylWgdyO97VbXvy7jujm5FsOCBbqi3A03wB138G5cJ+bc8gvNA5ozofcE/Hz8cl2++fRmft75s0132//9/T+umHgFN8y4gTFL8gYBhieE88WmL3h9zeu0mNSC2JRYp30sh9G3r35IWb7c1ZIYcmCUxaXMnav3S4cNc7UkrqVuXXjtNV0dsHVr+O03V0vkEGaFzaLtV23p/n33CwrDz8ePX277hfHXjOfLQV9S2bey4yc+e/bi8fTpsGuX9nwaMQK1bDm3tbub3U/s5qEOD+XpOjl0MvfNu4+aH9Zk+ZHcN9D72t534dhWxHpO+8dtrW6jul91B3wYJ9O1K9Svrx0uDG5DkZSFUqqSUqp0uyvs26e9gdrYHwXs0Xh7w7PP6u2AJk20sX/SJFdLZTfTd04nLDoMQXjzn4suml7Ki9f7vM7QlsXM55Qf+/frLc358/X7zz/X3k+7d8M330CVKoCuEljOK7eDYkZWBgv2LwB0zfY6/rm9pdrXac8dre7g2uBrL6yOctK4emPe6vMW/UL68Uy3vLUxYlNisYjFEZ/ScZQrByNHakeTlBRXS2Owkq83lFLKC7gLuBe4CkhDpxCPBhYD34jIwRKQs8jY5Q2VkqLz2Rg0mZk6QrxOHV1nwZ0i2ovI0dijtJvSjoT0BDY9somr6l/l3AlTUuDKKyE+XjsQNG5cpO5J6Ul8vvFz5u6dy5aILSS8mIB/ef9c14hIsfJgWcRCn6m6fO28O+dRtULVIo/hNFJTtdIoZ7z7S5rLeUMVpCzWAMvR6TV2i+hHEKVUDaAPcA8wT0RmOEVqO7A7KM+Qm/R0nU/KgxVFNhtPbWTevnm8d917zp/ss890puBly3Q8ix0kpSdRqbzjgia/2PgFY/7Sdo7uQd1Zdv8yh47vEOLidCGlUaNMDEYJUVzX2etE5E0R2ZmtKABEJEZEfheR24CZjhbW4IaUL68VxfbtOoNoRoarJSo2XYO6loyiENFbdz172q0oAIffyHMazFsEtqBCuQoOHd8hTJkCTzyhPaQiIlwtTZkm3zWeiFy4IyilqqOzwpbLcX5rzmsMZYAjR+Dnn3Um3nfecbU07s2RI9qIPWGCqyWxyau9X6W6X3V2Ru5kyuApeCk39Hd5/nkIDIQxY/R23nff6SSZhhKnUBHcSqk3geHoRH7ZHURE+jpPNPsw21BO5JFH4PvvYdo0uP9+V0vj3uzfD1dc4dZ778W1eZQo+/frmuRbtmingJEjXS1RqcXeCO5hwBUicq2I9LG+3FZRGJzM5Mlw7bW6GNPJkwVeXubIGSPQvLlbKwqw7XK7+DWI9Z8AACAASURBVOBiUjLcyBOpeXOdmv/FFy/WaTGUKIVVFruBas4UxOBBlC+vC/VYLLogkyE3f/+tkwIuXuxqSYrFd1u/48ZfbuT22beTkeVGu8y+vnrrs3Zt7aF3331w0C2dMUslhd2G6ozVIwrtPguAiLitijfbUCXAxo26WI+bPzmXOH366JvYkSNasXoQ2yK20fGbjhfev3rNq7zR5w0XSnQZTpzQ3z1fX51xoUn+ub8MhcfebaipwPvAe8DHOV6GskzXrlpRmOCpi2zYoG9ezz7rcYoCoEPdDrzU8yUAOtfrzNiuY10s0WVo2FBXiExN1Vui+/cX2MVgH4VdWawREY/KWW1WFiVEfLyOeO/bVxu83d1Q6mxuuUWnez9xQtec9kBEhE83fMrDHR52r0A9W+zcqbf8srJg1iz9PTTYhb0riy1KqXeVUt2VUh2zXw6W0eCJVKmiDd0zZuhcUpmZrpbIdcTEQGiorjjooYoCtMH7me7P2FQUbpcapG1bWLtWG8DLauGoEqKwK4tVNpqN66xBY7HAAw/o+IvOnWHp0rJTNOpSEhP11lwFNwxws5M1x9Ywbuk45t05j0bVGrlanNyIXFzVjh0LvXrp4kmXrnSzsmDVKu2Cm5UFN94I7YqWEr60U6x0H56MURYljAjMnq3jLp58Ej4uYyat9eu1wdUD7RSFISwqjJ4/9iQuNY46/nX4Z/g/NA1o6mqx8pKQoCPmd+7UtozPP9erD4DkZH18+HDuPh99pG1MBqCY21BKqfusyQQvd/4KpVRPRwho8HCU0mndV626GNkdFqaftEs7x45Bv37wf//nakmcxoFzB0hKTwKgim8VgqsFu1agy1G5sl41fPWVTgPfoYNeYURHQ8WKurDX7Nk659S5c3rLsF8/V0vtERSUSHAsMALYYn1FAxWAJkBv4CzwgjtmnjUrCxcjAq1aQWQkPPWUTtcQUAprS4voQkarV8PevdCggaslchqrjq7izjl3MmXwFG5teWuuc7/s+oW1J9byfI/n3WeLKiZGP7jMmqVzmuW3NSqiVyNmS6r421DW+hV9gR5AXSAF2AssEZETTpDVIRhl4QZs2ADvv6/rOFSqpP9xx+St5ubRzJ2rn1w//lg/pZZy4tPiqVy+cp6o75t/vZmFBxZSrUI1lt+/nE71OrlIwmLywQfwyiu6xkizZq6WxqUYm4XBdYSFwXPP6YjmVav0XnJpIDVVuw1Xrgxbt5bZ4MT0rHQCPgggMV1vOX4+4HPGdPWwh4LISO1R1a0bLFlSpl3A7XWdNRiKT+vWenUxbRr09qhwnfyJjNRba59+WmYVBYC38mbW7bMI8Avgk/6feJ6iAJ1C5I03tCefh6ZpcTZOW1kopSoA/6Ar65UD5ojIa0qpD4GbgHR0FtuHRCTO2udF4GEgCxgjIkut7Z2AnwA/dIW+sVKA4GZl4cYcPqxrUnft6mpJ7Ceny2YZ51zyuVw1vz2OjAxd/rZ6ddi8ucz+XV2xskgD+opIO6A9MEAp1Q1YBrQRkbbAAeBFq4Ct0CVcWwMDgMk56n1/BYwCmlpfA5wot8GZiOh63gMHwp49rpam+Eydqr1pyugNxRYerShAV4J8/nmIioIzZ1wtjdtRKGWhlPJVSt2jlHpJKTU++5VfH9Fk+036WF8iIn+LSHaY7wYgyHo8BPhNRNJE5ChwCOiilKoLVBGR9dbVxDTAadVP4pLTKa12HLdAKe2d4uOjXW09MafUunUwfLj24TdcFhFh/r75F1xuPYLhw3UCyLp1XS2J21HYlcUf6Jt5JpCU45UvSilvpdR2IApYJiIbL7lkBLDEelwfyFkc4ZS1rb71+NJ2p3Df9xvp8s4KRv+ylW0nYp01TdkmOBimT9eG72HDdH3vkmbZMh15XlQsFu0KHBSkn0INNjkUc4iBPw9k6MyhvPXPW64Wp/CUL6/tT6mprvleujGFVRZBInKniHwgIh9nvwrqJCJZItIevXroopRqk31OKfUyWvn8nN1ka4h82vOglBqllApVSoVGR0cXJJ5N7u/WiKuvCGDD4XPcPmU93/17xKw0nEH//ro+9aJFuuqes9mxQ0eXi1yshZAziC4qSgdyffut3l66HNOmwbZt2iW4kmNrYpcm1p5Yy9LDSwH4aP1H7In2oC3H8HAICSmZ76UnISIFvoBvgCsLc20+Y7wG/M96/CCwHqiY4/yLwIs53i8FuqNjO/blaL8b+Lqg+Tp16iT2EJecLqOmbZZGzy+Sl+bulOiE1GKNY7FYZPX+KJm08qDMCT0pCakZdslV6vjpJ5G0NH2cmOj48S0WkSlTRHx9RerWFYmK0m2PPSailEhoqEhmpkj37iJalYgEBoosWpR3rKwskZAQkS5d9BiGy5JlyZKeP/QUr9e95Mk/n5TYlFhXi1R4LBb9fQgOFklPd7U0JQ4QKrbu4bYa81wEe9DeS/uBncAuYGcBfWoC1azHfsC/wGC0cXoPUPOS61sDO9DeUyHAEcDbem4z0A29ylgCDCpIZnuVhYhIVpZF3lwYJsEvLJJmLy+Wl+ftlONnkwrdd/meMzLo83+k0fOLLryufO0veX/JXok8n2K3fKWKc+f0TXrChIvKw1527RK5+mr9Nb/hBpHIyIvnzp8XqVFDZNAg/T45WWTnTpEtW0TatdN9Pv4493ixsSJ33SUya5Zj5Cvl7InaI6GnQ10tRvFYuFB/B955x9WSlDiXUxaFzTprM35fRI7n06ctumiSN3q7a5aIvKGUOmRVCNlr/Q0i8pi1z8toO0Ym8LSILLG2d+ai6+wS4CkpQHBHus4ejk7k23+OMHfraTItFga0qUOX4Bo0rV2Z2lUqUKuKL5XKl+NMfCpbj8cSeiyGfw+e5cjZJBrU8OPpfs0Y0KYO+84k8P3aI/y1+wzlvLy4qV097urSgM6Nqtusg1ymOHtWR3f/+qtO9jZ1KrRvX/zxsrJ0upHslA8PPwxel+y6vvsuvPSStp20anWxPTVVR/OOHAlVq+qaz0OGwMsvF18eg2chAnfdBb//rlO59Cw7KfDsjuBWSrUDelnf/isiOxwon8NxRpxFZHwqP6w7yuzQU8QkXd745efjTcdG1bi9UxCD29bDxzv3Ter4uSS++/coc7eeIik9i+a1KzO6bxNualvXKI0FC3R9jPh4nfBt4MDC9z19WiuGN9/UeYB27ID69SEw0Pb1CQnQpYvOB/Trr5d3g73hBl1Xe8ECGDzYuMvagYggVpOj1+VzlLoH8fHQsSMMGgQTJ7pamhLDLmVhTSg4EphrbRoKfCMiXzhUSgfizKA8ESEqIY3D0YlEJ6QRGZ9KYmomNatUoF1QVVrWrZJHQdgiKS2TP3dG8N3aIxyITOSq4Oq8eUsbWtSp4hS5PYaICF1noFkz+O23/K/dsUNfN3kyTJigA6sWLtTV0xzF+fNQrZo+/uCDUp1d1pmkZ6XzwLwHmL1nNmuGr6FnQw94Wo+O1g8baWnaO6pK6f/ftFdZ7AS6i0iS9X0lYL3owDq3xJMiuC0WYfaWk7y3ZB/xqZk82D2Yp69vSpUKPq4WzXUkJOhYjAoVdDTtnj3amyktDR58EH76Saec7tRJ52UCrWAmToTGjR0vT/fuOjFiZCTUquX48Us5yw4v48klT3Lg3AEA5t85nyEthlw4LyI8vOBhfLx8aFu7LaO7jHaVqLZZvFh/v4YM0d+97IeHUsjllEVhE9oodAqObLKw7dJqKAZeXoo7r2rIDa3r8OHS/fz431EW7Ajnlvb16NCwOu0aVKV+Nb+ytUVVufLF4x9+gClTdA6mgABYuRLGjdM38Lfe0nvKffvq7SJnsWiRVmBGURSLTEsmcalxF97nPAaITo7mx+0/AtAisEWByiI8IZw31rzBgv0LCKkewroR6xwvdE6aNYMXXtDZhfv21TmkatZ07pxuRmFXFs+g3V3nWZtuAX4Skc+cKJtdeNLK4lJ2norj478PsP7wOdKzdOBYwxoVeaB7I+68qgGVy9qKw2KBmTPh7bfhwAEYP14boA0eRZYli5iUGKpWqEp579wVBbdGbKXTNzqteV3/uoQ/G37Zcc4ln6PjNx05cV5XSHjyqif5YlAJ7YgvWQK33qrjMJYt0zaxUoYjDNwdgZ7oFcU/IrLNsSI6Fk9WFtmkZ1rYfyaB7SdjWbgjgk3HYvD3Lcewzg14qEcwDWpUdLWIJY/FkteryeDxRCZG8ufBP0nNTCUhLYHne14+Ol5E+Gn7T4z5awyJ6Yl5trSczpo1MHSorjlfFAcMD6FYykIpVUVE4pVSNktMiUiMA2V0KKVBWVzKzlNxfL/2KH/ujCDTIlwVXJ2hHYK4o3NQoQzqBoMnMnP3TObsnUP/xv0Z0WEE3l46v+iR2CPsOLODm5rfRDmvEk4Rf/68dqsGSEoqVdH8xVUWi0RksFLqKLlTbCh0UkAnWBIdQ2lUFtlEnE9hdugp/twZwf7IBIIDKnJ7pyCubV6LVnWr4OVVhmwbhlJNliWLVpNbXTCMv9b7NSZcO8G1QuVkwQJ49FG9PWVPXJAbYSrllUJEhNX7o5m06hBbjuukh9Ur+nBrxyAe7hlCvWp+LpbQYLCP+fvmM3TmUABCqoWweeTmy6ZCz8jKYOXRlWRYMhjcbHDJCHjwIPTrp/ONbd5cKmwY9rrOrhCRfgW1uRNlQVnkJDI+lf8On2X53iiW7j6Dl5diRI8QHr/2Cqr6lTGDuKHUkGXJYuPpjczYOYPHOz/OlbWvtHndptObGPjzQGJSYmhTqw27Ht9VckKGhelyrK1awb//6sy1Hkxxt6EqABWBVcC1XHSXrQIsEZGWjhfVMZQ1ZZGT03EpfLx0P3O3naZ8OS86NqzGlfWr0qpeFVrVrUrjmpWMjcNQqkhMT6TmhzVJzUwFYPWDq+kdXIIlfOfN015S48fD66+X3LxOoLhxFo8CTwP1gC1cVBbxwJcOldDgMOpX8+OTO9szomcI87edZtOxGKatP05apnbDLV/OiyY1/WlYoyINavjRsEZFQgL9aVrbn1qVfR0ez2GxiLGjGJyKf3l/7m5zN3P3zuXFni/SpX6XkhVg6FBdOKkUU9htqKfcObWHLcryysIWmVkWjp5NYk9EPHvC49kfmcDJmGROxaZcUCIAVSqUo3/rOrw0qCU1KhV/OX0uMY0paw7z584IzsSnUr+6H3dd1ZDbOwWRlJbJ+iPn+O/QOfZExFPBx5u7uzTg3q6N8DZKxVBMopKi8FberivvWkrqsTsizqIN0AqokN0mItMcJqGDMcqicFgsQnRiGoejEjkQmUBYeDzzt5/G37ccLw1qyW0dg4q0KhARZm85xZsL95CUnkn/VnW4olYltp2I47/DuYsK1alSgQ4NqxFxPpXtJ+O4tnlNvrq3E37lvS8zusHgASxbBocOweOPu1qSYmGvgfs1tM2iFbAYGAisFZHbHSynwzDKovjsP5PAS/N2seV4LM1q+/P0dc0Y0LpOgUpj9+nzvLFoD5uOxtA1pAZvD72SJrX8c4277tBZKvl60yUkgOCAiiilEBFmbDjO+AVhdAsJYOqILpQvZ2wqBg9l2DCd2nz5cujTx9XSFBl7lcUuoB2wTUTaKaVqA9+JyE2OF9UxGGVhHxaLsGhXBJ8vP8Dh6CQaB1ZicNu6pGVaOBWXQnR8Gr4+XgT6++LjrQgLjycsPJ4alcrzbP9m3H1VwyLbKX7fcopnZ+/g3q4NeXuoba8Xg6GwxKTE8OrKVxnacijXNb6u5CZOSIDOnbU77e7d4OdZLuz2JhJMERGLUipTKVUFiALcNiDPYD9eXoqb29XjxivrsmhnOFP/O8bElYcoX86LoGp+BFb2JTEtk6Nnk0jLtNA4sBIvDWrBnVc1LLar7m2dgjgQlcDXa44Q4O/Lk32amBWGoVisOrqKO2bfwbmUc6w8tpIdj+3Ik4/KaVSurOu59+sH773n8d5R2RRWWYQqpaoB36K9ohKBTU6TyuA2eHsphrSvz5D29UlJz8K3nJdTPZueu6EFEXGpTFxxkD93hvPK4Fb0bBJIdEIaX685zO7weBRQt5ofwQEVaVW3Cq3qVaFB9YrG48pwgRaBLUjP0gXK9p/dzz/H/ynZ1UXfvnDPPVpZPPAAXHFFyc3tJIocwa2UCgaqiMhOZwjkKMw2lGezYm8kry/cw4mYZMp7e5GeZcHHW9G5UQ0EITwulVOxyVisX19/33Lc0LoOE25uVfay8hpsMnHjRMavGs+vt/3KwKYuSPh35gysWKHLs3p7jtOGvTaLP4CZwB/ZBZDcHaMsPJ/UjCxW7I1ix6k4qvr5cHO7erky7aZmZHEgMoG9EfFsOxHHnC2naF6nMj8+dBW1KlfIZ2RDWUBEOBV/igZVG7haFI/CXmXRG7gTuBG9/TQTWCQiqY4W1FEYZVH2WLU/iidmbCWwcnmmPtSFxjX9C+5kKHOsOLKCJjWa0Khao5KZ8MsvYft2+PbbkpnPTi6nLAplPRSRNSLyBNqo/Q0wDG3kNhjchj7Na/HrqG4kpWUxZNI6/tod4WqRDG5GSkYKIxaMoOePPdkTvadkJj17Fr77DrZsKZn5nEShXU2UUn7AbcBjwFXAVGcJZTAUl/YNqrHwqZ40rlmJx2ZsZfwfuzmXmOZqsQxuwsSNEzlx/gSn4k9x0683kWXJKriTvTz9NFSvrvNGeTCF8oZSSs0EugJ/oXNCrRYRS/69DAbXUL+aH7Me6867i/cxdf0x5mw5xf3dGjHymsYE+vu6WjybZGRZOBiZyN6IeMLjUsiwCNUr+nBNs5pcYbbTHEanep3wK+dHSmYKb/V560IhJadStSo89xy8+CKsX69rx3sghbVZDACWiUgJqGHHYGwWBoBDUQlMWnmIBTvC8S3nzR2dg3ige3CuyHJXkZaZxezQUywNO8PGozGkZ9p+/urUqDr3dm3ILe3rG/dgB7D9zHZ+2PYDnw/43OFJMy9LYiI0bgzt2ul0IG6MvQbuisAzQEMRGaWUago0F5FFjhfVMRhlYcjJ4ehEJq86zMKd4SDwwsAWPNQjuORuFpcQn5rBo9O2sP7IOUICK9G3RS3aBlWldb0qBFWviG85LyLOp7JwRzizt5ziUFQiXUJq8MmwdgRVL4O110sDv/8ONWvCNde4WpJ8sVdZzEQH4z0gIm2s9ov1IuK2dQSNsjDY4mxiGi/8vovleyO5u0tDJtzcCt9yJesDHxmfyoM/bOJQVCIf3N6WoR3q56u0ciZn9PZWfDKsHX1b1LZ5bWpGFjFJ6VSuUM7EmxQBESEsOow2tdq4WhSXY6+yCBWRzkqpbSLSwdq2Q0Ta5dOnAvAP4Iu2jcwRkdeUUjXQrrfBwDFgmIjEWvu8CDwMZAFjRGSptb0T8BPgh05kOFYKENwoC8PlEBE+XLqfyasPExJYiVcHt7zszdfRJKRmcMuX6zhzPpWv7uvENc1qFrrvsbNJPPHzVvZExDOyVwjdrwjg6NlkDkUlcjgqkUPRicQkpV+4Pqi6H9c2r8mIHiHGjTgftkZs5bllz7HyqE4LcrlqfA7h/Hl4+WW45Ra4rgQjyouAvcriP6AfsE5EOiqlrgB+FZHLVhhR+lGpkogkKqV8gLXAWOBWIEZE3lNKvQBUF5HnlVKtgF+BLuhiS8uBZiKSpZTaZO27Aa0sJorIkvxkNsrCUBCr90fxxqI9HIlOolfTQJ4f0II29as6bb6MLAuPTd/C6gPRTH+4C1dfEVjkMVIzsnh94R5+3XTiQlu1ij40reVPk1r+BFWvSI1K5YlNTmf7iTjWHIgmPcvCoCvrMvraJrSqV8WRH6lUcOMvN7L44GIA7m5zNz/f+rPztifT0qBpU6hXTxu73bD+hb3K4nrgFXSK8r+BHsBwEVldyMkropXF48A04FoRiVBK1UV7VjW3rioQkXetfZYCE9Crj1Ui0sLafre1/6P5zWmUhaEwpGdamLb+GJNWHSIuOYP+rWrzaO/GdGpUwyHjiwgr90WxfG8kG4/GcCQ6iTdvacP93ewLCDsYmUBcSgYhgZUIqFT+sje36IQ0flh3lBnrj5OYnsndXRry/A0tqFrRbFFlsytyF+2/bo+IsOGRDbmq7GVaMjked5zG1Rs7ToF8/TU89pg2dLvh6sIRxY8CgG7o0qobRORsIfp4o20dTYAvrSuIOBGpluOaWBGprpSaZB13hrX9e2AJWlm8JyLXWdt7Ac+LyGAb840CRgE0bNiw0/Hjxwv12QyG8ykZfP/vEaauP875lAy6hNTgvVuvtGv7JjYpnRfm7mRpWCRV/XxoWbcyw68OZkCbug6UvHCcT8lg4oqD/PTfMar5+fDSoJbc2jF/W0lZ4sN1H3Iy/iQTB07M1b4lfAudv+1My8CW/Hb7b7St3db+yVJToVkzCAiA0FC3yxtVLGWhlOqY36AisrWQk1cD5gFPoYsm2VIWX6KN5jmVxWLgBPDuJcriuYJqaZiVhaE4JKdnMnPzSSauOIi3l+K3Ud1oUqtykcdJSc9i8Bf/ciImmf+7oTkjeoRQztv16dbDws/zyvzdbDsRR9eQGrx1Sxua1i765yuNZFoyKeeVO/Rs8ubJjF48GoD6letzaMwhKpRzQN6x2bN1kaTJk92uol5x61l8nM85AfoWZnIRiVNKrQYGAJFKqbo5tqGy04acAnJm/AoCwq3tQTbaDQaHU7F8OR7qEUKvpjW565sNPPjDZhY82YMAazCfiHAgMpEluyNoFFCRW9rbfjp//699HI5OYtqILkUyYjub1vWq8vtjVzMz9CTvLdnHwM//ZeQ1jRnTt2mZL2d7qaIASExPBKBy+crc3PxmktKTHKMsbr8d3n9fpzL3EIqcorzQAytVE8iwKgo/tK3jfaA3cC6HgbuGiDynlGoN/MJFA/cKoKnVwL0ZvSrZiF5tfCEii/Ob36wsDPay42Qcd0xZT5YIFa03UhFITMu8cM3wq4OZcHPrXP3WHTrLvd9ttHnOnTiXmMY7i/fx+9ZTBAdU5Iu7O3JlkPMM/J7KtohtNA1oin/5suFRVtxtqOdE5APr8R0iMjvHuXdE5KV8+rZF54/yRuegmiUib1htH7OAhugtpjtEJMba52VgBJAJPJ3t8aSU6sxF19klwFPGddZQEuw6dZ4/tp8mK8fXrXFNfwa0rsOXqw7x03/H+GF45wuutxHnU7jpi3VU8SvHn0/18oin9fWHz/HMrO2cTUzj+QEtGH51sFtsmZUJQkNhzhx491238YwqrrLYKiIdLz229d7dMMrC4GzSMnV22/C4FF67qTX1q/vx6vzdRJxPZf7oq4tl63AVsUnpPPf7TpbtiaRBDT/u7qLTi9Sr5ln1oz2OyZNh9GhYuxZ69HC1NEDxlUXOILwLx7beuxtGWRhKguPnkhg3cztbT8QBULuKLx/f0Z6eTYseQ+FqRIRleyL59t8jbD4Wi1LQNaQGQzvUZ0Drusbd1oqIIAheygGrr6QkaNBA2y7mzLF/PAdgVhYGg5PIsgjL90ZyPiWDQVfWxd+3sKXt3ZcT55KZv/0087ed5sjZJHy8Fb2b1eSmdvW4rmVtKpWCz1hUjsUdY/qO6UzbOY0vB31J/yv6O2bgF16ADz+EQ4cgJMQxY9pBcZVFFpCEjq3wA5KzTwEVRMRtHzWMsjAY7EdE2HX6PAt3hLNwRwRn4lOp4ONFv5a1ualtPa5tXpMKPu5vl3EE4/4ax2cbPwOgd6PerHpwlWPiVE6d0kriqafgk0/sH89OiuU6KyJl41tgMBhsopSibVA12gZV48WBLQk9HsuCHadZvOsMf+6MoGGNiky6pwNtg6oVPJiHM7rLaCZtnkSmJZOIxAjOJp+lZiUHuEUHBemI7lq17B/LiTjNddbVmJWFweA8MrMsrDkQzavzdxOdmMYLA1sywoUp30uKCasnEFQliOHth9uMyygN2J3uw9MwysJgcD5xyen8b/ZOlu+NpG+LWnx4e9sLAYyGImKxwF9/wQ03uDQFyOWUhXGmNhgMxaZaxfJ8+0AnJtzUirUHzzLw839ZsTeS0voQ6lQWL4Ybb4Q//nC1JDYxKwuDweAQwsLPM+bXbRyOTqJ9g2rc0TmIq68IpFGNiqW6HGx0UrRjbBdZWTp9ef368O+/9o9XTMw2lMFgcDrpmRbmbDnFV2sOcTImBYCK5b1pYq230bpeVfq3qk2DGp5fGjYyMZJXVr7Cz7t+Zt+T+2hYtaH9g372GYwbB5s3Q+c89+sSwSgLg8FQYogIR88msfFoDAciEzgUlcihqEQizqcC0C6oKoOurMugK+t6rOK4fvr1LD+yHICRHUfyzU3f2D9ofLz2jrrpJvj5Z/vHKwbFzTprMBgMRUYpReOa/nnqgZw4l8zi3REs3hXBu0v28e6SfTSsUZEuITXoElKDriE1aFijokd4Vb3Y88ULyuJo3FGyLFl4e9lpmK5SBR55BBYt0lX1fN3HWcCsLAwGg0s4GZPMsj2RbDx6jk1HY4hNzgCgVmVf+rWsxTPXN6dmZfe5Wdpi3F/jGNxsMH1D+jpOwSUmgp+fyzyizDaUwWBwW0SEQ1GJbDgaw8Yj5/h7TyT1qlZg+sNdPXabym5S9ZYdFRxQP6MIGNdZg8HgtiilaFq7Mvd3a8Skezry68huxCZncOtX/7E3It7V4pU8ERHQqBH88IOrJbmAURYGg8Ht6NSoOrMf6463UjzwwyaiE9JcLVKhSMtMY2/0XvsHqlMHGjbUKczdZPfHKAuDweCWNKtdmZ9GXEV8SgZjf9tGlsU9bpqX42jsUXr+2JM+U/sQmRhp32BKwaOPQlgYrF/vGAHtxCgLg8HgtrSoU4U3b2nDf4fP8fmKg64W57JkWbIY8PMAQsNDiUyKZOxfY+0f9K67wN8fvv7a/rEcgFEWBoPBrRnWuQG3dwrii5UHWXMg2tXi2MTby5uJAyYC4OPlQ5MaTbCIxb5ByGiZ5QAAGJFJREFU/f3hoYfgl1/g5EkHSGkfxhvKYDC4PSnpWQydvI4z8aksfLKn23pIvb/2fQY3G0zrWq1ztYtI8Vxro6Nh507o189BEhaM8YYyGAwei195b76+vxMWi/DYjC2kZmS5WiSbPN/z+TyKAuDeufdy+6zbWXl0ZdGSLNaseVFRrFwJ5887SNKiY5SFwWDwCBoFVOKzu9oTFh7Py/N2e0xm2+ikaObsmcPve3+n37R+bDq9qeiDREXpjLSPPuoy7yijLAwGg8fQt0VtxvZryu9bTzFj4wlXi1Molh5eSoZFR6d3D+pOl/pd8lxToOKrVQvGj4eZM2HqVGeIWSBGWRgMBo9ibL+m9GlekzcWhrHleKyrxSmQ+9reR9gTYTzW6THG9x6fx3axO2o33b7vxrnkc/kP9PzzcM01OittRIQTJbaNMXAbDAaP43xyBjdNWktaZhZ/jb2G6pXKu1qkYrE1Yiv9p/fnXMo5hjQfwu/Dfs8/GeGBA9C2rc5KO3u2U2QyBm6DwVBqqFrRh8n3duRcYjqvLwxztTjF5uT5k5xL0SuK5UeWs+/svvw7NGsG774LXbuWuO3CaSnKlVINgGlAHcACfCMinyul2gNTgApAJvCEiGyy9nkReBjIAsaIyFJreyfgJ8APWAyMlWIsiTIyMjh16hSp2Qm6yhgVKlQgKCgIHx8fV4tiMNhNm/pVGd2nCZ+vOMiNbetxfavarhapyAxpMYRnuj3DL7t/YfE9i216UuVh3DjnC2YDp21DKaXqAnVFZKtSqjKwBbgF+Az4VESWKKUGAc+JyLVKqVbAr0AXoB6wHGgmIllKqU3AWGADWllMFJEl+c1vaxvq6NGjVK5cmYCAAI/Il+9IRIRz586RkJBASEiIq8UxGBxCeqaFmyetJS45gzXPXYtvOdek9bYHi1iISoqijn+donX87TfYtAk++gi8HLdJVOLbUCISISJbrccJwF6gPiBAFetlVYFw6/EQ4DcRSRORo8AhoItV6VQRkfXW1cQ0tNIpMqmpqWVSUYDO6hkQEFBmV1WG0kn5cl68NKglZ+JTWbSj5I2+jsBLedlUFAU+yB88CJ9+qoslZTk/7qREbBZKqWCgA7AReBr4UCl1EvgIeNF6WX0gZ0z7KWtbfevxpe225hmllApVSoVGR9tOC1AWFUU2ZfmzG0ovvZoG0qy2P9/+e8RjYi8KIjIxkl4/9mJbxLbLX/Tqq/Daa/DjjzB8uNNtGE5XFkopf+B34GkRiQceB8aJSANgHPB99qU2uks+7XkbRb4Rkc4i0rlmzZr2C28wGNwepRSP9GzMvjMJrDtUgPupB3Au+RzXTb+OdSfX0WdqH1YdXXX5iydM0EpjxgxYvdqpcjlVWSilfNCK4mcRmWttfhDIPp6NtlGAXjE0yNE9CL1Fdcp6fGm7R6KU4tlnn73w/qOPPmLChAlFGmP16tX8999/F94PHz6cOXPmOEpEg8HjGNKhHjUr+/L1P4ddLYrdnDh/glPxejMlKSOJLClgi+mll3Tti03FiAwvAk5TFkrveXwP7BWRT3Kc+v/27j06qvpa4Ph35w1JeITwDhBQaUGTBlGeotHrRVBBWkBAeS3ackG5go+64LpUXF1cqa0g2KrFIlhF0C6gItorgiAQEXlF3q9CLI9gCFoMjyQk2fePcxIGSBhIMhlmZn/WOouZ33nM3rN0ds7vnPP7HQXucF/fBZSOO7wEGCwi0SLSGrgB+FpVs4E8EeniHnM48KGv4va16OhoFi1aRG5ubqX2LyoquqRYGBPqoiPCGdktmTX7ctl5NLBn1uvQtAOrRqyiaVxT/nz/n7m7zd2X3yEmBnbtch7a8yGf3ToLdAeGAdtEJNNt+x/g18AMEYkA8oHRAKq6Q0Q+AHbi3FL7qGpZSR3L+Vtn/+EuVfLCRzuq/T+q9s3q8Hyfy9/6FhERwejRo5k+fTpTpky5YN23337LqFGjOH78OA0bNmTOnDm0bNmSkSNHkpCQwJYtW0hISCAjI4Pw8HDeffddXn31VQBWr17NtGnTOHbsGC+99BIDBgyo1tyMudYN7dyK11bu540v/snMIR38HU6V/KzJz9g9bjd1out43xigtjsKb1YWJCf7JCZf3g21VlVFVVNVNc1dPnHbO6rqz1S1s6pu8thniqpep6o/8bw1VlU3qupN7rpxlXnG4lry6KOPMm/ePE5eNILkuHHjGD58OFu3buXhhx/mscceK1u3d+9eli9fzsKFCxkzZgyPP/44mZmZ9OjRA4Ds7GzWrl3L0qVLmThxYo3mY8y1oG7tSIZ2bcXSrUfJyj3t73CqrLxCoar862QFY2K99x60aeMMae4DvjyzuKZ5OwPwpTp16jB8+HBmzpxJrVq1ytrXrVvHokXO5Zxhw4bx9NNPl60bOHAg4eEV30Per18/wsLCaN++Pd99V8UpHY0JUL+6rQ1zM7J4c80Bpvw8xd/hVKsz587w649+zaf7P2Xzf22mZd2WF27QqxfUqeNc8P6w+nvqbbgPP5kwYQKzZ8/m9OmK/wLyvNU1Njb2sseLjo4uex3gJ17GVFrD+GjuS23KksyjnC28Nue8qKwBHwzgvW3vceLsCQb+bSAFRQUXbpCQ4Dyo98c/+uTzrVj4SUJCAg8++CCzZ88ua+vWrRsLFiwAYN68edx2223l7hsfH09eXl6NxGlMoBl0SwvyCor4ZFtgPqRXkWd6PENEmNMZ1Di2MScLypkIqVcvaNHi0vZqYMXCj5588skL7oqaOXMmc+bMITU1lXfeeYcZM2aUu1+fPn1YvHgxaWlprFmzpqbCNSYgdGqdQHKD2ry/0f/zVlen7i2782afN/nkoU9YMmQJjWIb1ejnh9QQ5bt27aJdu3Z+iujaYN+BCQUzlu/jlRV7WT/pP2hUJ8bf4QQUG6LcGBMy7kttgios+SZgn9+95lixMMYEnesbxXNzy3rMW/8vSkqCs/ekVHZezVybsWJhjAlKI7olczD3NGv2V260hGtdxr8yuOvtu0j7cxqnC33/XIkVC2NMUOp9U1MS46L565dZ/g6l2p0rPseQhUNYmbWSnNM5vLj2RZ9/phULY0xQiooI46FOLfh8Tw6Hvj/j73CqVWR4JM/e/iwAMRExdG7e2eefacXCGBO0BndynnJeuPmwly0Dzy9v/iX9ftqPZUOX0ecnfXz+eVYsatiUKVO48cYbSU1NJS0tjfXr11/1MWyIcmOuTLN6tejSugELNx+mOMgudIdJGIsHLaZHqx4183k18ikGcMZ+Wrp0KZs3b2br1q0sX76cFpV42tKGKDfmyo3olsyh78/yf9uP+TuUgBbaxSI9/dLltdecdWfOlL9+7lxnfW7upeu8yM7OJjExsWwcp8TERJo1a8aKFSvo0KEDKSkpjBo1ioICZ8yX5OTksie8N27cSHp6OllZWbzxxhtMnz79gie4V69eTbdu3WjTpo2dZRjjoWf7xiTVr8WCDRWM1mquSGgXixrWs2dPDh06RNu2bXnkkUf44osvyM/PZ+TIkbz//vts27aNoqIiXn/99QqPkZycbEOUG3MVwsKEX9ycxNr9uWSfPOvvcAJWyA5RDlx+ztratS+/PjHxque8jYuLY9OmTaxZs4aVK1cyaNAgJk2aROvWrWnbti0AI0aM4E9/+hMTJky4qmPbEOXGVKz/zc2ZuWIfi7cc4ZH06/0dTkAK7WLhB+Hh4aSnp5Oenk5KSgpvv/12hdtGRERQUlICQH5+/mWPa0OUG1OxVg1iuaVVfRZtPsLYO667YPh/c2WsG6oG7dmzh3379pW9z8zMpHHjxmRlZbF//34A3nnnHe64w5miPDk5mU2bnIkEFy5cWLafDVFuzNXr3zGJ/Tmn2HaknKG9jVdWLGrQqVOnGDFiBO3btyc1NZWdO3cydepU5syZw8CBA0lJSSEsLIwxY8YA8PzzzzN+/Hh69OhxwSx5NkS5MVev901NCBNYvivH36EEJBuiPMTYd2BC2S9ey+BMYTEfjutOdETF0xSHMhui3BgT8h7u3Irdx/KYuHCbv0MJOFYsjDEho3/HJCbcfQOLtxzhqwMn/B1OQLFiYYwJKWPuuI4GsVHMXnvQ36EEFCsWxpiQEhMZzoCOSazcncMPpwv9HU7AsGJhjAk5fdOaUVSifLytZmaZCwY+KxYi0kJEVorILhHZISLjPdb9t4jscdtf8mifJCL73XX3eLR3FJFt7rqZYk/UGGOqoH3TOtzQKI4PM4/4O5SA4csziyLgSVVtB3QBHhWR9iJyJ/AAkKqqNwJ/ABCR9sBg4EagF/CaiJTe2/Y6MBq4wV16+TBunxIRhg0bVva+qKiIhg0bcv/99wOwZMkSpk6dWu6+cXFxNRKjMcFOROjfMYkNWT+Qeejf/g4nIPisWKhqtqpudl/nAbuA5sBYYKqqFrjrSp+QeQBYoKoFqnoQ2A90EpGmQB1VXafOQyF/Bfr5Km5fi42NZfv27Zw96wxo9tlnn9G8efOy9X379rWBAI2pAUO7tKJ+7UheWb7X36EEhBq5ZiEiyUAHYD3QFughIutF5AsRudXdrDlwyGO3w25bc/f1xe3lfc5oEdkoIhuPHz/uNa7JqyYjLwjygjB51eRL1j/56ZNl61/+8uVL1o/+aHTZ+lmbZnn9vFK9e/fm448/BmD+/PkMGTKkbN3cuXMZN24cAAcPHqRr167ceuutPPvss1d8fGOMd3HREYy+/TpW7TnOpm9/8Hc41ebkmXM+Oa7Pi4WIxAELgQmq+iPO4IX1cbqmfgN84F6DKO86hF6m/dJG1Vmqeouq3tKwYcNqid8XBg8ezIIFC8jPz2fr1q107lz+/Lnjx49n7NixbNiwgSZNmtRwlMYEv+FdW9EgNorff7o74AfgVFX++Pk+er7yhU+GYvdpsRCRSJxCMU9VF7nNh4FF6vgaKAES3XbPaeOSgKNue1I57QErNTWVrKws5s+fz7333lvhdhkZGWVnHZ7XOYwx1SM2OoLH/7MtXx34nsVbAvdid+ahfzPwjXX8YdleurRpQKP4mGr/DJ8NUe6eLcwGdqnqNI9VfwfuAlaJSFsgCsgFlgDvicg0oBnOheyvVbVYRPJEpAtON9Zw4NXqiHFy+mQmp0+ucP3L97zMy/dc2v1UalafWczqc+XdT5769u3LU089xapVqzhxouInSe3GL2N866FOLVm85QgvfLQTgLQW9WhWrxYxkdf22FElJcrnu3N4c80B1h/8nsS4KF78RQoP3tKC8LDq/93w5XwW3YFhwDYRyXTb/gd4C3hLRLYDhcAI98L1DhH5ANiJcyfVo6pa7O43FpgL1AL+4S4BbdSoUdStW5eUlBRWVTCJUvfu3VmwYAFDhw5l3rx5NRugMSEiLEyY/mAaw99azxMffFPWnhAbRaP4aOrXjqJe7Ujqx0aRUDuK+rFR1IoMJzoijPAwoUSV4hJFFYpVKSwq4XRhEWcKisk/V0xRiXKuuISiYkVRoiPCiYkMc44RGU5MpPM+JiKcuJgIEuOiaRQfTeM6MURFOJ0/qkpeQREnThWSe6qAjVk/8LeNhziQe5pmdWN45t52DO7UgviYSJ99Tz4rFqq6lvKvNwAMrWCfKcCUcto3AjdVX3T+l5SUxPjx4y+7zYwZM3jooYeYMWMG/fv3r6HIjAk9LRvUZvkTd7ArO4+93+WRffIsR0/mk/NjASfPFrIv5xQ/nC7khzOFlFzhpY0wgeiIcCLDhcjwMCLCBUEoKCom/1wJ+UXFeLtMkhgXRVR4GLmnCyksKrlgXYeW9Zg5pAO9b2pCZLjv71WyIcpDjH0HxlReSYnyY/458s+VUFBUTHGJEh4mhIkQFiZlBaJ2lHPmcbluZFWloKiEArdw5J8rJi+/iON5BeTk5XPsZAHHfjxLYZGSGB9FYmw0ifFRNIiNpm3jeJrUrf7rElDxEOU2raoxxlyhsDChXu2oajmWiLhdUOHUxXfdR9XFxoYyxhjjVcgVi2DtdrsSoZy7MaZqQqpYxMTEcOLEiZD80VRVTpw4QUyMb/o5jTHBLaSuWSQlJXH48GGuZCiQYBQTE0NSUpL3DY0x5iIhVSwiIyNp3bq1v8MwxpiAE1LdUMYYYyrHioUxxhivrFgYY4zxKmif4BaR48C3ldw9EWdww1BiOYcGyzk0VCXnVqp6yRwPQVssqkJENpb3uHsws5xDg+UcGnyRs3VDGWOM8cqKhTHGGK+sWJSvcjMaBTbLOTRYzqGh2nO2axbGGGO8sjMLY4wxXlmxMMYY45UVCw8i0ktE9ojIfhGZ6O94qouIvCUiOe6856VtCSLymYjsc/+t77Fukvsd7BGRe/wTddWISAsRWSkiu0Rkh4iMd9uDNm8RiRGRr0XkGzfnF9z2oM25lIiEi8gWEVnqvg/qnEUkS0S2iUimiGx023ybs6ra4ly3CQf+CbQBooBvgPb+jquacrsduBnY7tH2EjDRfT0R+J37ur2bezTQ2v1Owv2dQyVybgrc7L6OB/a6uQVt3jhz3se5ryOB9UCXYM7ZI/cngPeApe77oM4ZyAISL2rzac52ZnFeJ2C/qh5Q1UJgAfCAn2OqFqq6Gvj+ouYHgLfd128D/TzaF6hqgaoeBPbjfDcBRVWzVXWz+zoP2AU0J4jzVscp922kuyhBnDOAiCQB9wF/8WgO6pwr4NOcrVic1xw45PH+sNsWrBqrajY4P6xAI7c96L4HEUkGOuD8pR3UebvdMZlADvCZqgZ9zsArwNNAiUdbsOeswDIR2SQio902n+YcUvNZeCHltIXifcVB9T2ISBywEJigqj+KlJees2k5bQGXt6oWA2kiUg9YLCI3XWbzgM9ZRO4HclR1k4ikX8ku5bQFVM6u7qp6VEQaAZ+JyO7LbFstOduZxXmHgRYe75OAo36KpSZ8JyJNAdx/c9z2oPkeRCQSp1DMU9VFbnPQ5w2gqv8GVgG9CO6cuwN9RSQLp+v4LhF5l+DOGVU96v6bAyzG6Vbyac5WLM7bANwgIq1FJAoYDCzxc0y+tAQY4b4eAXzo0T5YRKJFpDVwA/C1H+KrEnFOIWYDu1R1mseqoM1bRBq6ZxSISC3gbmA3QZyzqk5S1SRVTcb5f/ZzVR1KEOcsIrEiEl/6GugJbMfXOfv7qv61tAD34tw180/gGX/HU415zQeygXM4f2X8EmgArAD2uf8meGz/jPsd7AF6+zv+SuZ8G86p9lYg013uDea8gVRgi5vzduA5tz1oc74o/3TO3w0VtDnj3LH5jbvsKP2t8nXONtyHMcYYr6wbyhhjjFdWLIwxxnhlxcIYY4xXViyMMcZ4ZcXCGGOMV1YsTMgSR27p6Jwi0lREVERu89jmuIg0uMrjThCR2lWIa6T7uZkeS/tKHKdKcRjjyYqFCRoiEuU+pHRF1LlvfD3Q1W3qhvOcQjf3eD8BclX1xFWGMgGo6o/0+6qa5rHsrMQxKhWH59DWxpSyYmECnoi0E5GXcR44anuVu2fgFgf332lcWDy+dD/jNyKyQUS2eswTESsiH7vzR2wXkUEi8hjQDFgpIiurmNoFRCRORFaIyGZ3LoMHfBTHIPc4T4lIw+rMwQQuG0jQBCT3DOJBnKfRBZgDpKozHPnV+BJ4zn3dCXge5y9ycIpFhoj0xBkioZP7WUtE5HagIXBUVe9zY6qrqidF5AngTlXNrXSCzg/2bR7vuwL5wM/VGRAxEfhKRJbgjP9UbXGo6hsi8jEwElgtIjtwhv9epqoll93ZBC07szCBKhunUPxKVbur6l8qUSjAGSOng1t8ItWZD+KAiFzP+TOLnu6yBdgM/BSneGwD7haR34lID1U9WfW0ylzcDXUWp1D9r4hsBZbjDDPd2BdxqOohVf0tzsQ5s93l71U9rglcVixMoBoAHMEZhvs5EWlVukJEOntcGO4rIlNK3198EFU9gzMZzCicQgDwFc44Uo1wurYEeNHjh/t6VZ2tqnuBjjg/1i+KyHMXH7+aPYxzNtNRVdOA74CYq43jSr8fEekEvAa8CvwNmOSDnEyAsG4oE5BUdRnO5C8NgKHAhyKSi3OmsR5I89h8Cc5AahXJwOl6muy+Xwe8C3ylqioinwK/FZF5qnpKRJrjDMoYAXyvqu+KyCmcbhuAPJypXKvSDVWeujhzN5wTkTuBVgAi0uxq4vD2/bjdbn8AjuGcUYxXZ/ZIE8KsWJiA5t6pNAOY4f4lXFyJw2QA43GKBDhnGEm403Sq6jIRaQesc0Y+5xROgboe+L2IlOAUj7Hu/rOAf4hItqreWanELr1m8QgwD/hIRDbijKJbOuFNSjXHcQLoo6rfVjJ2E4Rs1FljjDFe2TULY4wxXlmxMMYY45UVC2OMMV5ZsTDGGOOVFQtjjDFeWbEwxhjjlRULY4wxXv0/yBrCq8lmMgYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "topo = np.loadtxt('../../data/topo.asc', delimiter=',')\n",
    "plt.plot(topo[0,:],  label='North')\n",
    "plt.plot(topo[-1,:], 'r--', label='South')\n",
    "plt.plot(topo[int(len(topo)/2),:], 'g:', linewidth=3, label='Mid')\n",
    "plt.title('Topographic profiles')\n",
    "plt.ylabel('Elevation (m)')\n",
    "plt.xlabel('<-- West    East -->')\n",
    "plt.legend(loc = 'lower left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can convert this code into a command-line Python script in two different ways:\n",
    "\n",
    "* Export the Jupyter notebook as a `.py` file [File -> Download As -> Python (.py)]\n",
    "* Copy the script and paste it into a simple text file (using a text editor like TextWrangler or Notepad++) and save the file with a `.py` extension\n",
    "\n",
    "Go ahead and convert your Jupyter notebook into a `.py` file using whichever method is easier. Call your file `topo_profiler.py` and make sure that the file is in the same directory that contains the folder `data` where the file `topo.asc` is. Then open the file `topo_profiler.py` in your chosen text editor. Most code-friendly text editors will color the text in the file according to the language they are written in!\n",
    "\n",
    "If you exported the file through the Jupyter notebook menu, any Markdown (text) cells should be prefixed with a `#`. These are comments and the Python interpreter will not read them. You should also see some lines that look like this: `# In[ ]:`. These are line numbers from the Jupyter notebook and are also commented out.\n",
    "\n",
    "Open a new Bash window and navigate (using `cd`, `ls` and `pwd`) to the directory that contains your `topo_profiler.py` file. To run the script from the command line, type:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$ python topo_profiler.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Python read the contents of the text file `topo_profiler.py` and tried to run the script, but it produced an error:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Traceback (most recent call last):\n",
    "  File \"topo_profiler.py\", line 19, in <module>\n",
    "    get_ipython().magic(u'matplotlib inline')\n",
    "NameError: name 'get_ipython' is not defined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you copied and pasted the code from the notebook to the text file instead of exporting it, it produced this error instead:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "  File \"topo_profiler.py\", line 20\n",
    "    %matplotlib inline\n",
    "    ^\n",
    "SyntaxError: invalid syntax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The iPython magic command `%matplotlib inline` makes plots appear within the Jupyter notebook instead of in a separate window, but the regular Python interpreter doesn't understand iPython magic commands. Go into the text file and comment out this line. In many text editors, the shortcut Command-/ (or Control-/) will comment out a line. You can also just add a # at the start of the line.\n",
    "\n",
    "Now run the script again. Python opened up a window that shows the figure we could see inside the notebook. There are some tools along the bottom of this window that you can use to navigate and save the figure.\n",
    "\n",
    "Notice that the Bash window doesn't show a `$` before the text prompt while the figure window is open. This shows that Bash is \"busy\" and won't respond until you close the figure (you can open a new Bash window if you need to run something while the figure is open). The Python code is also paused -- it will wait until the figure is closed to continue past the `plt.show()` line!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Deprecation warnings\n",
    "\n",
    "The Bash window might be showing a message like this:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "2016-05-09 13:31:40.589 python[15942:2318986] setCanCycle: is deprecated.  Please use setCollectionBehavior instead"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A command that's deprecated is one that you are discuraged from using because it's been replaced by something else. The command still runs, though, but it might stop working in the future. If the deprecated command is i code, you should fix it so it stops warning you and doesn't break later on. If it's not in your own code, then ignore the warning and update your Python distribution soon."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Close the figure window. The Python script will continue running until it reaches the end and exits. You should now have the `$` prompt back in your Bash window. This means that you can run commands again.\n",
    "\n",
    "If you want your script to open multiple figure windows at the same time, you need to number your figures using `plt.figure()`. Let's modify the script so it makes separate figures for each profile instead of a single one. We only need call `plt.show()` once, after all of the figures have been defined:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "# get_ipython().magic(u'matplotlib inline')\n",
    "\n",
    "topo = np.loadtxt('../../data/topo.asc', delimiter=',')\n",
    "\n",
    "plt.figure(0)\n",
    "plt.plot(topo[0,:], label='North')\n",
    "\n",
    "plt.figure(1)\n",
    "plt.plot(topo[-1,:], 'r--', label='South')\n",
    "\n",
    "plt.figure(2)\n",
    "plt.plot(topo[int(len(topo)/2),:], 'g:', linewidth=3, label='Mid')\n",
    "\n",
    "plt.title('Topographic profiles')\n",
    "plt.ylabel('Elevation (m)')\n",
    "plt.xlabel('<-- West    East -->')\n",
    "plt.legend(loc = 'lower left')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only figure 2 has a title, legend, and axis labels because they are defined only once, while figure 2 is active.\n",
    "\n",
    "Go back into the script and change it so it only produces one plot with all three profiles, as it did before. Run it again to check.\n",
    "\n",
    "For a short script running locally, we can easily use the buttons in the figure window to save our figure to a file. However, this is not practical if the script takes a long time to run or if we have to run multiple scripts in a row. It is also not always possible to interact (or even see!) the figure window when running code on a remote server. To save the figure automatically, we can replace `plt.show()` by the Matplotlib `savefig` function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.show()\n",
    "plt.savefig('../../media/Your_profiles.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Instead of opening a figure window, your script will create a new file inside the `data` directory and run continuously to the end of the script."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting memory buffer\n",
    "\n",
    "You might want your script to save the figure and still show it as an interactive figure window. Every plotting command in our script adds data to a memory buffer. Waiting until all lines are plotted before showing the figure is more efficient than repeatedly redrawing the figure. The function `plt.close()` (or closing the interactive figure window) empties that buffer. If we want to save the figure, we need to include the `plt.savefig()` command before the memory buffer empties.\n",
    "\n",
    "If your script creates and saves multiple figures, it is often a good idea to include `plt.close()` after `plt.savefig()` to force the memory buffer to empty and let your script run faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
